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Domain shift is a problem commonly encountered when developing automatedhistopathology pipelines. The performance of machine learning models such asconvolutional neural networks within automated histopathology pipelines isoften diminished when applying them to novel data domains due to factorsarising from differing staining and scanning protocols. The Dual-ChannelAuto-Encoder (DCAE) model was previously shown to produce featurerepresentations that are less sensitive to appearance variation introduced bydifferent digital slide scanners. In this work, the Multi-Channel Auto-Encoder(MCAE) model is presented as an extension to DCAE which learns from more thantwo domains of data. Additionally, a synthetic dataset is generated usingCycleGANs that contains aligned tissue images that have had their appearancesynthetically modified. Experimental results show that the MCAE model producesfeature representations that are less sensitive to inter-domain variations thanthe comparative StaNoSA method when tested on the novel synthetic data.Additionally, the MCAE and StaNoSA models are tested on a novel tissueclassification task. The results of this experiment show the MCAE model outperforms the StaNoSA model by 5 percentage-points in the f1-score. Theseresults show that the MCAE model is able to generalise better to novel data andtasks than existing approaches by actively learning normalised featurerepresentations.

Multi-Channel Auto-Encoders and a Novel Dataset for Learning Domain Invariant Representations of Histopathology Images